Project ID: 2007CA215GTitle: A Bayesian approach to snow water equivalent reconstructionProject Type: ResearchStart Date: 6/01/2008End Date: 5/31/2010Congressional District:Focus Categories: Hydrology, Surface Water, Water SupplyKeywords: Snow; Data Assimilation; Remote Sensing; ModelingPrincipal Investigators: Molotch, Noah Paul (University of California, Los Angeles); Margulis, StevenFederal Funds: $ 61,312Non-Federal Matching Funds: $ 62,102Abstract: Climate in the semi-arid Western U.S. exhibits considerable inter-annual variability; and the temporal and spatial distributions of precipitation, snowmelt, soil moisture, evapotranspiration, streamflow, and other hydrologic processes are sensitive to this variability. Sensitivity to climate change varies across gradients of physiography (e.g. elevation, vegetative community structure, and latitude) but the drivers and degree of this sensitivity in different mountainous landscapes are not fully comprehended. Similarly, the impact of these changes on basin-scale snowpack water storage cannot be determined because observations are not distributed across a range of elevations and other physiographic conditions that control snow distribution. As a result, statistical interpolation models of these scarce observations inadequately represent spatial patterns of snow accumulation. For nearly three decades, remotely sensed observations of snow cover depletion have been used to forecast seasonal snowmelt runoff and (indirectly) seasonal snow accumulation integrated over a watershed. The use of these data to reconstruct snow accumulation is based on the simple concept that deeper snow takes more time (or energy) to melt than shallower snow. More recently advances in remote sensing have enabled sub-pixel detection of snow cover depletion and the development of pixel-specific snow accumulation reconstruction models. The scarcity of ground-based observations needed to evaluate model performance and refine algorithms has restricted reconstruction modeling studies to small headwater catchments. Similarly, reconstruction techniques have not been used to resolve the temporal variability in snow distribution during the accumulation season. To address these inadequacies the proposed research will synergistically develop new observing and modeling systems for estimating the spatial distribution of snow accumulation.

The proposed work will address compelling questions related to temporal and spatial variability in snow distribution patterns in the central Sierra Nevada Mountains. A new method of snowfall estimation will be developed in which an Ensemble Kalman Smoother will be used to assimilate new remotely sensed snow measurement capabilities into physically based mass and energy balance models. Densely distributed clusters of ultrasonic snow depth sensors spanning the elevational gradients of the seasonally snow covered portions of the Sierra Nevada will be used to develop this new technique by accounting for both sub-grid variability and the spatial representativeness of the ground observations. The combination of these new modeling and measurement capabilities will enable new understanding of the spatial snow accumulation processes.

Knowledge of interannual spatial patterns of precipitation is crucial in understanding how spring streamflow magnitude timing will be modulated by a changing Sierra Nevada climate. Furthermore, the techniques developed under this research may be a critical step in making spatially distributed runoff forecast models operational. Such a step is critical given that current statistically based runoff forecast models perform poorly under anomalous climatic conditions, which may occur with increased frequency in the future due to climate change. Finally, knowledge of the spatial patterns of snow accumulation may lay the foundation for an inter-decadal spatially-explicit reconstruction of Sierra Nevada climatology based on all the available remote sensing, meteorological, and in situ measurements of snowpack properties.